Not Just A Black Box : Interpretable Deep Learning by Propagating Activation Differences

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How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, methodology): “…al., 2017], techniques for extracting local explanations from black-box models [Ribeiro et al., 2016, Lei et al., 2016, Adler et al., 2016, Selvaraju et al., 2016, Smilkov et al., 2017, Shrikumar et al., 2016, Kindermans et al., 2017, Ross et al., 2017], and visualization [Wattenberg et al., 2016].”
Focal Onset Seizure Prediction Using Convolutional Networks (2018, methodology): “One example is the application of deep learning and automatic feature extraction to genomics data, yielding novel insights into patterns in DNA sequences [1].”
Accountability of AI Under the Law: The Role of Explanation (2017, unknown): “…an explanation of the system’s behavior overall, is known as local explanation [Ribeiro et al., 2016, Lei et al., 2016, Adler et al., 2016, Fong and Vedaldi, 2017, Selvaraju et al., 2016, Smilkov et al., 2017, Shrikumar et al., 2016, Kindermans et al., 2017, Ross et al., 2017, Singh et al., 2016].”